AI News, LWA 2010 artificial intelligence

Legal informatics

Policy issues in legal informatics arise from the use of informational technologies in the implementation of law, such as the use of subpoenas for information found in email, search queries, and social networks.

But in more complex models of legal service delivery other actors or automated processes may moderate the relationship between a client and their attorney making it difficult to tell which communications should be legally privileged.[2]

Artificial intelligence is also frequently employed in modeling the legal ontology, 'an explicit, formal, and general specification of a conceptualization of properties of and relations between objects in a given domain'.[4]

Artificial intelligence and law (AI and law) is a subfield of artificial intelligence (AI) mainly concerned with applications of AI to legal informatics problems and original research on those problems.

and the need to store and retrieve large amounts of textual data has resulted in contributions to conceptual information retrieval and intelligent databases.

Formal models of legal texts and legal reasoning have been used in AI and Law to clarify issues, to give a more precise understanding and to provide a basis for implementations.

In the late 1970s and throughout the 1980s a significant strand of work on AI and Law involved the production of executable models of legislation, originating with Thorne McCarty's TAXMAN [9]

LEGOL was used to provide a formal model of the rules and regulations that govern an organization, and was implemented in a condition-action rule language of the kind used for expert systems.

showed that the natural language of legal documents bears a close resemblance to the Horn clause subset of first order predicate calculus.

showed that logic programs need further extensions, to deal with such complications as multiple cross references, counterfactuals, deeming provisions, amendments, and highly technical concepts (such as contribution conditions).

As the 1990s developed this strand of work became partially absorbed into the development of formalisations of domain conceptualisations, (so-called ontologies), which became popular in AI following the work of Gruber.[30]

These, however, have not been widely adopted as the basis for expert systems, perhaps because expert systems are supposed to enforce the norms, whereas deontic logic becomes of real interest only when we need to consider violations of the norms.[34]

In any consideration of the use of logic to model law it needs to be borne in mind that law is inherently non-monotonic, as is shown by the rights of appeal enshrined in all legal systems, and the way in which interpretations of the law change over time.[41][42][43]

In order to better evaluate the quality of case outcome prediction systems, a proposal has been made to create a standardised dataset that would allow comparisons between systems.[52]

Though predictive coding has largely been applied in the litigation space, it is beginning to make inroads in transaction practice, where it is being used to improve document review in mergers and acquisitions.[53]

Other advances, including XML coding in transaction contracts, and increasingly advanced document preparation systems demonstrate the importance of legal informatics in the transactional law space.[54][55]

Current applications of AI in the legal field utilize machines to review documents, particularly when a high level of completeness and confidence in the quality of document analysis is depended upon, such as in instances of litigation and where due diligence play a role.

Predictive coding leverages small samples to cross-reference similar items, weed out less relevant documents so attorneys can focus on the truly important key documents, produces statistically validated results, equal to or surpassing the accuracy and, prominently, the rate of human review.

However, to work more efficiently, parts of these services will move sequentially from (1) bespoke to (2) standardized, (3) systematized, (4) packaged, and (5) commoditized.[58]

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